class_docstr: Generate views by random transformation (augmentation) on given batched graphs, 
  where each graph in the batch is treated independently. Class objects callable via 
  method :meth:`views_fn`.
  
  Args:
      candidates (list): A list of callable view generation functions (classes).
docstr: Method to be called when :class:`RandomView` object is called.
  
  Args:
      batch_data (:class:`torch_geometric.data.Batch`): The input batched graphs.
      
  :rtype: :class:`torch_geometric.data.Batch`.  
docstr_tok: ["Method","to","be","called","when",":","class",":","`","RandomView","`","object","is","called",".","Args",":","batch_data","(",":","class",":","`","torch_geometric.data.Batch","`",")",":","The","input","batched","graphs",".",":","rtype",":",":","class",":","`","torch_geometric.data.Batch","`","."]
code: def views_fn(self, batch_data):
      r"""Method to be called when :class:`RandomView` object is called.
  
      Args:
          batch_data (:class:`torch_geometric.data.Batch`): The input batched graphs.
  
      :rtype: :class:`torch_geometric.data.Batch`.
      """
      data_list = batch_data.to_data_list()
      transformed_list = []
      for data in data_list:
          view_fn = random.choice(self.candidates)
          transformed = view_fn(data)
          transformed_list.append(transformed)
  
      return Batch.from_data_list(transformed_list)
  
file_path: DIG/dig/sslgraph/method/contrastive/views_fn/combination.py
imports: import random
  from torch_geometric.data import Batch
label_desc: Use image node_id 3 for calling the RandomView obj's underlying member method code with example usage: obj.views_fn(batch_data) and returns: Batch